This model is a fine-tuned version of pyannote/speaker-diarization-3.1 on the speaker-segmentation dataset. It achieves the following results on the evaluation set:

Loss: 0.3735
Model Preparation Time: 0.0099
Der: 0.1228
False Alarm: 0.0205
Missed Detection: 0.0096
Confusion: 0.0927

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 43
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Der	False Alarm	Missed Detection	Confusion
0.4492	1.0	570	0.4525	0.0099	0.1515	0.0219	0.0094	0.1202
0.3875	2.0	1140	0.4229	0.0099	0.1403	0.0212	0.0098	0.1093
0.4014	3.0	1710	0.3889	0.0099	0.1324	0.0207	0.0102	0.1016
0.3656	4.0	2280	0.3899	0.0099	0.1305	0.0212	0.0094	0.1000
0.3232	5.0	2850	0.3804	0.0099	0.1283	0.0207	0.0096	0.0981
0.319	6.0	3420	0.3796	0.0099	0.1256	0.0204	0.0097	0.0956
0.3018	7.0	3990	0.3750	0.0099	0.1247	0.0202	0.0101	0.0945
0.2928	8.0	4560	0.3700	0.0099	0.1220	0.0203	0.0097	0.0920
0.3321	9.0	5130	0.3753	0.0099	0.1238	0.0204	0.0098	0.0936
0.2738	10.0	5700	0.3735	0.0099	0.1228	0.0205	0.0096	0.0927

Framework versions

Transformers 4.50.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

whitneyten/pydiarize-Dataset-2_4

speaker-segmentation-fine-tuned-datasetID-hugging_2_4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

whitneyten/
pydiarize-Dataset-2_4